Total Factor Productivity Across The Developing World
In the last
three decades, many studies have analyzed the relative contribution of factor
inputs and technical progress to economic growth. Since the seminal work of
Solow (1957), total factor productivity—defined as the efficiency with which
firms turn inputs into outputs—has been considered as the major factor in
generating growth. Especially the availability of firm level data sets allowed
researchers to investigate the reasons behind vast dispersion in productivity
performances across firms which led to establishing policies that would improve
productivity and eventually generate growth. Some early examples of firm-level
productivity analyses are Bailey, Hulten, and Campbell (1992) and Bartelsman
and Dhrymes (1998) for U.S. manufacturers and Roberts and Tybout (1996) for a
number of developing countries.
comparison of productivity performances across countries has been limited due
to the unavailability of a homogenous data source. This note aims to fill this
gap. It uses a data set which has been collected through surveys conducted
across a large number of developing countries. The homogenous nature of the
data provides a unique opportunity to compare average productivity performances
of firms across industries, countries and regions.
The World Bank’s
Enterprise Surveys 
provide a unique source of information that can be used to measure TFP across a
large set of developing countries. The
data used for TFP analysis in this note covers manufacturing firms in 80
countries from different regions of the world.
All data used in this analysis were collected from surveys conducted since
2006, with the exception of India which was surveyed in 2005. The regional
coverage of the countries is presented in Table 1. The table also shows the number of firms that are included in the
analysis from each region.
The data cover
all the major two-digit manufacturing industries according to the International
Standard Industrial Classification (ISIC), revision 3.1. For this analysis some
industries are combined to achieve a sufficiently large number of observations
(Table 2). Industries were grouped together based on similarities in the
type of activity and factor intensity. The group “Other Manufacturing” is a
residual category that includes all firms that are outside the six major
industry groups. The concentration of
firms in six major industry groups is the result of a sample design, used in
most countries, where selected industries were targeted to facilitate
production function with three factors of production—capital, labor and
intermediate goods—is used to estimate TFP.
Firm sales are used to measure output; the replacement value of machinery,
vehicles and equipment is used to measure capital; labor is assessed by the total
compensation of workers including wages, salaries and bonuses; and intermediate
goods are determined by the cost of raw material and intermediate materials.
TFP is estimated as the residual term of the production function.
The TFP values
used in this note are compared with the values obtained from five additional
production function specifications. These specifications are three variations
of the Cobb-Douglas production function; a transcendental logarithmic
(trans-log) production function with capital, labor and materials as input
factors; and a non-parametric cost-based Solow residual method.
The first variation of the Cobb-Douglas production function adds energy costs
to the input factors; the second variation uses only labor and capital as input
factors; and the third uses value added as the dependent variable instead of
total sales. Details of the analysis with these alternative TFP measures are
discussed in Saliola and Seker (2010).
That study showed that TFP estimates obtained from all specifications are
positively and highly correlated with each other.
values are estimated separately for each country, while controlling for
industry differences by including dummy variables for each industry listed
above. All monetary values are converted into U.S. dollars and then deflated by
GDP deflator in U.S. dollars (base year 2000).
For each variable used in the estimation, values that are three
standard deviation away from the mean value for each country are
excluded from the analysis. These outlier tests are performed at the country
level and by industry (manufacturing and services). Firms that have material
cost-output or labor cost-output ratios that are three standard deviations away
from the mean are also excluded from the analysis.
In addition, Afghanistan, Albania, Burkina Faso, Kosovo, Malawi, Niger
and West Bank and Gaza were excluded since at least one of the variables required
to compute TFP was not available for at least 30 percent of the manufacturing
When the data is collected, each firm is
assigned a sampling weight in order to allow the data to be representative at
These weights are not used in the TFP analysis because the variables to measure
TFP are not available for all firms included in the surveys. Hence the
composition of the sample adopted in the empirical analysis to measure TFP
might not reflect the composition of firms in the manufacturing sectors. The
un-weighted sample for which TFP analysis could be performed is defined as productivity sample. The data coverage
issue raises the question whether productivity sample over- or under-samples
firms in certain size groups. In order to test this difference, size
distribution measured in terms of employment levels in productivity sample is
compared to the distribution in full sample obtained by using the survey
weights (which is defined as weighted
sample). The weighted sample includes the productivity sample and the rest
of the firms for which TFP could not be estimated and it is representative of
the manufacturing sector in each country. In general, the distribution of
productivity sample appears to mirror relatively well the distribution of
weighted sample. In countries where there is reasonable difference (more than
10 percentage points in any size group), small firms (less than 20 workers) are
slightly under-sampled in productivity sample. In a few countries like Indonesia, Nepal, Uzbekistan and Guatemala this difference is around
obtained from the estimation using a Cobb-Douglas production function can be
interpreted as input factor elasticities, i.e., they show the responsiveness of
sales to changes in the levels of each input factor used in the production. In
the estimation of the production function, raw materials and intermediate goods
have the highest elasticity in 52 of the 80 countries.
In 51 countries, labor has the second highest level of elasticity after
material. The average elasticity values across countries are 0.10 for capital,
0.46 for labor, and 0.54 for materials. Figure 1 presents
elasticities for select countries. Share of capital
is lowest in Indonesia with a value of 0.02 which means that a 10 percent
increase in capital is associated with an increase in output of just 0.2
percent. Across all countries, sum of factor elasticities vary around one.
The input factor
elasticities obtained from the estimation yield
comparable results to several other studies. Using firm-level data from
Colombia covering the years 1982-1998 and using the same estimation method as
above with four input factors—capital, labor, energy and materials—Eslava et al. (2004) find factor elasticities of 0.08,
0.24, 0.12 and 0.59. The estimation using Enterprise Surveys data for Columbia
from 2006 yields the factor elasticities in respective order of 0.09, 0.48, 0.07,
and 0.46. Hallward-Driemeier, Iarossi
and Sokoloff (2002) calculate these elasticities as
0.15, 0.30, 0.24 and 0.31 for Malaysia using firm-level data covering
1996-1998. In our results for 2007 Malaysian data, these values are 0.03, 0.48,
0.10, and 0.51 respectively. Differences in these elasticities could be a
result of changes in the time period studied or differences in the definition
of capital. Eslava et al. (2004)
estimate production function at industry level rather than country
level. This could also play a role in getting different elasticities.
Using the factor
elasticities obtained above for each country, firm-level
TFP values were computed. Firms’ productivity levels are weighted by their output
shares in order to compute aggregate productivity. Output
shares are calculated as the ratio of each firm’s sales to aggregate sales in
the country. Hence, when weighted productivities are aggregated to compute the
aggregate productivity, a firm with higher production has a larger contribution
than a firm with low production. Simple average productivities are also
presented in order to see how an average firm performs in each country.
The year in
which the surveys were conducted vary in the data. This difference can
contribute to variation in productivity performances across countries. For
analytical purposes, countries were grouped in two cohorts—those surveyed in 2006-2007
and those surveyed in 2008-2009 (44 and 36 countries respectively). The
cross-country comparison in this section uses data from countries that have
relatively large sample sizes. Comparison of average and aggregate
productivities shows noticeable differences across countries. A country with a
high average productivity level could have quite low aggregate productivity or
vice versa. This discrepancy between the two measures could be caused by the
differences in the size distribution of the samples. Small sample size in a
particular size group, which is more likely to be the case for large firms,
could cause noticeable differences across both TFP measures. Another reason for
this discrepancy is the variation in average productivity levels of firms at different
size groups. If small firms are much more productive than large firms in a
country, then this country might have high average productivity but low
aggregate productivity relative to other countries.
Figure 2 shows
aggregate and average productivity values in the countries that were surveyed
from 2008-2009 and that had at least 100 firms for which TFP could be
estimated. Among these countries, Indonesia has the highest aggregate
productivity followed by Turkey. The picture is quite different for average
productivity. Brazil has the highest average productivity among these
countries. Serbia, which has the lowest aggregate productivity level, has an
average productivity that is higher than the average
productivities in Indonesia or Turkey.
The same analysis is performed for those
countries that were surveyed in 2006-2007 and that had more than 200 firms for
which TFP could be estimated (Figure 3). Peru
has the highest aggregate productivity among these countries. However, average
productivity is among the lowest in this country. The difference between
average and aggregate productivities could be caused by how productivity is
distributed among firms at different size levels. Productive large firms make
large contribution to aggregate productivity. Thus a country with low aggregate
productivity but high average productivity could have many productive small
firms. However, this difference could also be caused by the distribution of
firms in productivity sample. For example, in Nicaragua the share of
large firms in the sample is 3.5 percent (only 9 firms), one of the lowest
shares in the 2006-2007 period. These large firms have very low productivities
which drag the aggregate productivity to lower levels as compared to average
productivity. While the firm-size distribution for Nicaragua is representative
of the population (share of large firms is 4.5 percent in weighted sample), the
small number of observations causes the big discrepancy between the two TFP
coverage of data from the ECA, LAC and AFR regions allows performance of
regional-level analysis (Table
Using all countries for which TFP could be estimated, countries are ranked
according to their aggregate and average productivity levels. In the ECA region
Hungary has the highest aggregate productivity which is followed by Romania and
Uzbekistan. However, the ranking for average productivity is quite different.
Hungary has the lowest average productivity. Among the large economies in the
region—Ukraine, Turkey, Russia, Bulgaria and Kazakhstan—Turkey has the highest
aggregate productivity level.
In the LAC
region, Peru has the highest aggregate productivity, followed by Mexico. The
least productive country is Honduras although average productivity in this
country is the second highest in the region. In this region all countries
except Brazil were surveyed in 2006. In AFR, 21 of the 25 countries included in
the analysis were surveyed in 2006-2007. Among these countries, Ethiopia has
the highest aggregate and average productivity levels. On the other hand, Zambia
has the lowest aggregate productivity but the second highest average
productivity. The other four countries in this region—Cameroon, Côte d’Ivoire,
Madagascar and Mauritius—were surveyed in 2009. The country with the highest
aggregate productivity in this group is Côte d’Ivoire (with a TFP of 0.76)
followed by Madagascar (with a TFP of -0.04).
The spread of average productivity
distributions shows variation across these three regions (Figure 4). The dispersion in the AFR
region is the smallest among the three. The standard deviation of TFP values in
AFR is 0.39 whereas it is 0.64 and 0.71 in LAC and ECA respectively. The
difference in log productivity levels between the 5th and 95th
percentiles in the AFR region is 1.2, which corresponds to a TFP ratio of 3.3.
These ratios are 7.4 in LAC and 9.4 in ECA. Figure 5 shows the cumulative density of
average TFP in each region.
The graph indicates that all regions had similar average productivity. Moreover,
the productivity distribution in ECA and LAC are more spread out than the
distribution in AFR region. This means that the number of very high and very
low productive firms in these two regions is higher than the number in AFR
In Asia, there
are five countries surveyed in 2009—Indonesia, Mongolia, Nepal, Philippines and
Vietnam. Average TFP value of these countries is 0.03. Nepal has the highest
aggregate productivity level (0.38) which is followed by Indonesia (0.27).
Lowest aggregate productivity is observed in Vietnam (-0.004). Comparing
average productivities, Indonesia has the top ranking (0.05) which is followed
by Philippines (0.04).
manufacturing industries listed in Table 2 are
likely to have different production technologies. Therefore, separate
estimations at the industry level are not only desirable but they could be
useful in understanding differences in firm performance as well as revealing
comparative advantages within countries.
Industry-level estimates of TFP
values are presented only for those countries that had at least 45 observations in each selected industry—food, garments and chemicals.
The countries for which industry-level TFP values could be computed in
2008-2009 period are presented in Figure 6. The cross-country comparison of aggregate productivities shows that Brazil,
which has the
second highest average productivity in the food industry, has the highest aggregate productivity. In addition, in the garments
and chemicals industries Brazil shows the lowest aggregate productivity but the
highest average productivity. Egypt, Arab Rep has the highest aggregate
productivity in the chemicals industry and it ranks second to last in food and garments.
Comparison of average productivities shows that Turkish manufacturers have
the second lowest productivity in all three industries.
As mentioned earlier, the
discrepancies between average and aggregate productivities could be caused by
differences in firm-size distributions within the samples and average
productivity levels at different size groups. For example, Philippines have the
highest average productivity in the food industry, but exhibit the lowest
aggregate productivity level. In the Philippines sample, the share of firms with
more than 100 employees in food industry is relatively small (9 firms) and they
have relatively low productivities.
Table 4 presents a comparison of aggregate
and average TFP for the group of countries surveyed in 2006- 2007.
Chile has the highest aggregate productivity in the food industry although the average productivity is quite low (thirteenth to last).
Bolivia shows the highest aggregate productivity in garments while Peru is the
country with the highest average productivity (Figure
has highest aggregate productivity in chemicals although the average
productivity is second to last. Mexico exhibits relatively good performance in
garments and chemicals industries. Firms in Mexico have the third highest
aggregate productivity and the fourth highest average productivity in garments,
and the second highest aggregate productivity and the third highest average
productivity in chemicals.
provides an analysis of the total factor productivity for firms in developing
countries from different regions of the world using the World Bank’s Enterprise
Surveys. It presents cross-industry, cross country and regional productivity
comparisons. Indonesia has the highest aggregate productivity among the
countries that were surveyed in 2008-2009, followed by Turkey, while Brazil has
the highest average productivity. Among the countries that were surveyed in
2006-2007, Peru has the highest aggregate productivity among these countries.
However, average productivity is among the lowest in this country. The regional
analysis shows some variation across ECA, LAC and AFR regions in terms of
average productivity distributions. The dispersion of total factor productivity
in AFR is the smallest among the three regions. The analysis across industries
shows how countries vary in the productivity performances of their industries. In
2008-2009 Brazil stands out for having the highest average productivity in the garments
and chemicals industries and the second highest average productivity in the
food industry. Among the countries that were surveyed in 2006-2007, Mexico
exhibits relatively good performance in garments and chemicals. Firms in Mexico
have the third highest aggregate productivity and the fourth highest average
productivity in garments, and the second highest aggregate productivity and the
third highest average productivity in chemicals.
Bailey, M. N., C. Hulten, and D. Campbell. 1992. "The Distribution of Productivity in Manufacturing
Plants." Brookings Papers on Economic Activity: Microeconomics.
Bartelsman, E. J. and P. J. Dhrymes.
1998. “Productivity dynamics: U.S. manufacturing plants, 1972–
1986.” Journal of Productivity Analysis
Eslava, M., J. Haltiwanger, A. Kugler and M. Kugler. 2004. “The Effects
of Structural Reforms on Productivity and Profitability Enhancing Reallocation:
Evidence from Colombia.” Journal of
Development Economics 75(2):333-371.
Hallward-Driemeier, M., G. Iarossi, K. L. Sokoloff. 2002. “Exports and
Manufacturing Productivity in East Asia: A Comparative Analysis with Firm-Level
Data,” NBER Working Paper No: 8894.
Roberts, M. and J. Tybout. 1996. Industrial Evolution in
Developing Countries: Micro Patterns of Turnover, Productivity and Market
Structure. New York: Oxford University Press.
Saliola, Federica and Murat Seker (2010), “Productivity analysis
using micro level data from Enterprise Surveys,” Working Paper, Enterprise
Analysis Unit, World Bank.
“Technical change and the aggregate production function.” Review of Economics and Statistics 39(3): 312-320.
data used in this study as well as the methodology used in data collection and
sample construction are available at www.enterprisesurveys.org.
 The countries included in the analysis, by region, are:
Eastern Europe and Central Asia (ECA):
Armenia; Azerbaijan; Belarus; Bosnia and Herzegovina; Bulgaria; Croatia; Czech
Rep.; Estonia; Macedonia, FYR; Georgia; Hungary; Kazakhstan; Kyrgyz Rep.;
Latvia; Lithuania; Moldova; Poland; Romania; Russian Federation; Serbia; Slovak
Rep.; Tajikistan; Turkey; Ukraine; Uzbekistan; Middle East and North Africa (MENA): Algeria; Egypt, Arab Rep.;
Jordan; Morocco; Syrian Arab Rep.; Yemen Rep.; Latin America and the Caribbean
(LAC): Argentina; Bolivia; Brazil;
Chile; Colombia; Ecuador; El Salvador; Guatemala; Honduras; Mexico; Nicaragua;
Panama; Paraguay; Peru; Uruguay; South and East Asia and Pacific (Asia): India; Indonesia; Malaysia;
Mongolia; Nepal; Pakistan; Philippines; Thailand; Vietnam; Sub-Saharan Africa (AFR): Angola; Botswana; Burundi;
Cameroon; Côte d’Ivoire; Congo; Dem. Rep.; Ethiopia; Ghana; Guinea; Guinea-Bissau;
Kenya; Madagascar; Mali; Mauritania; Mauritius; Mozambique; Namibia; Nigeria;
Rwanda; Senegal; South Africa; Swaziland; Tanzania; Uganda; Zambia. Indicator
Surveys (IS) were excluded because of the small size of the sample.
 The Cobb-Douglas production function
specification used in the estimation is where K is capital, L
is labor, and M is material input. The exponents represent factor elasticities.
 In the non-
parametric Solow residual method, output elasticity of each input factor is
calculated as the cost share of that input in total cost. TFP is estimated as
the residual of the production function, making use of the calculated
 The paper is
available from the authors upon request.
Exchange rates and GDP deflators are obtained from World Development Indicators,
total, 3381observations were identified as outliers.
 See A Note on Weights on www.enterprisesurveys.org for more details on the weight calculations and the use
of weights with Enterprise Surveys.
9 Elasticity values
of the 80 countries are available upon request.
 In this study the
value of capital stock is measured by the replacement cost of machinery,
vehicles and equipment.
11 More countries were
surveyed in 2006-2007 than in 2008-2009 and many of the countries in the
2006-2007 survey had sample sizes above 100 observations. Hence 200
observations is used as a cutoff only to make the
graph easier to read.
 (TFP(95pct))/TFP(5pct) =e^1.2=3.3
13 The upper and lower
tails of the cumulative density graphs are trimmed in order to have a better
illustration of the central part of the TFP distribution across regions.
 These industries
were chosen due to their relatively higher coverage across countries. The
analysis was also performed for textile, non metallic and metal and machinery
industries. Additional results are available upon request.
 Among the countries
surveyed in 2006 – 2007, 30 countries meet the 45 observations criterion in the
food industry, 19 countries in garments and 8 countries in chemicals.